An information processing apparatus may access another apparatus via a network to browse some data objects (e.g., document data) stored in that apparatus. As the amount of stored data grows, it takes more time and effort for users to reach their desired data objects. A data search system may therefore be used to aid the users. For example, a data search system accepts input of one or more keywords from a user and retrieves data objects containing the exact keywords or relating to the keywords.
Web pages are an example of such document data. Some web page search engines are designed to present a list of web pages with their ranking information. The ranks of web pages represent their popularity, importance, or other properties. Search engines may arrange the found web pages in descending order of the ranks, so that the web pages with higher rank values will be seen at upper locations.
PageRank (registered trademark of Google Inc.) is known as an algorithm for ranking web pages. A web page may have some links pointing to other web pages. The PageRank algorithm determines the relative positions of web pages on the basis of their backlinks and forward links. Here the term “backlink” refers to a link received by a web page from another web page. The term “forward link” refers to a link embedded in a web page which points to another web page. The link relationships among web pages may be represented by, for example, an adjacency matrix. Actually, what is done to determine the ranking of web pages with PageRank is to solve an eigenvector problem of an adjacency matrix (or modified adjacency matrix). For example, the eigenvector of a matrix may be calculated by using an algorithm called the power method.
Because the number of web pages is enormous, the PageRank method would take a long time to calculate the ranks of all web pages with accuracy. To reduce the computational cost, one proposed method calculates approximate ranks, instead of the exact ranks. The proposed method equally divides and distributes an approximate PageRank value of a collected web page to other pages to which it links. This distribution to linked pages is repeated over and over at each receiving page. Reduction of computational costs is achieved by discontinuing the repetition when an appropriate limit is reached.
The following is an example of related documents:    U.S. Pat. No. 6,285,999, Specification    Page et al., “The PageRank Citation Ranking: Bringing Order to the Web.,” [online], Oct. 30, 2001, The Stanford University InfoLab, [Found on Feb. 23, 2012], Internet “URL:http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf”    Yamada et al., “Efficient Collection Strategies of Important Web Pages by Incremental PageRank,” IPSJ Journal: Computing System, Information Processing Society of Japan, October 2004, Vol. 45, No. SIG 11, p. 465-473
As described above, there is a method for determining evaluation scores (e.g., ranks) of a plurality of data objects (e.g., documents) based on evaluation scores of other data objects, as well as on their links. The method updates the evaluation scores by repetitively redistributing a change in the scores from one data object to linked data objects until the update reaches an appropriate midway point.
The above method permits the effect of an update of evaluation scores to propagate through the links of data objects. This propagation, however, takes place only in a limited range because it is interrupted midway. Evaluation scores of data objects outside the range could therefore be inaccurate due to the lack of influence from their related data objects. Without compensation for this lack of influence, such an inaccurate score would simply be retrieved in response to a reference request.